متابعة
Himabindu Lakkaraju
Himabindu Lakkaraju
Assistant Professor, Harvard University
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عنوان
عدد مرات الاقتباسات
عدد مرات الاقتباسات
السنة
Human decisions and machine predictions‏
J Kleinberg, H Lakkaraju, J Leskovec, J Ludwig, S Mullainathan‏
The quarterly journal of economics 133 (1), 237-293, 2018‏
16902018
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods‏
D Slack, S Hilgard, E Jia, S Singh, H Lakkaraju‏
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020‏
1075*2020
Interpretable Decision Sets: A Joint Framework for Description and Prediction‏
H Lakkaraju, SH Bach, J Leskovec‏
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016‏
10232016
Faithful and customizable explanations of black box models‏
H Lakkaraju, E Kamar, R Caruana, J Leskovec‏
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 131-138, 2019‏
682*2019
Mining big data to extract patterns and predict real-life outcomes.‏
M Kosinski, Y Wang, H Lakkaraju, J Leskovec‏
Psychological methods 21 (4), 493, 2016‏
3282016
" How do I fool you?" Manipulating User Trust via Misleading Black Box Explanations‏
H Lakkaraju, O Bastani‏
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 79-85, 2020‏
3252020
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective‏
S Krishna, T Han, A Gu, S Jabbari, S Wu, H Lakkaraju‏
2492023
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes‏
H Lakkaraju, E Aguiar, C Shan, D Miller, N Bhanpuri, R Ghani, ...‏
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015‏
2322015
Towards a Unified Framework for Fair and Stable Graph Representation Learning‏
C Agarwal, H Lakkaraju, M Zitnik‏
Conference on Uncertainty in Artifical Intelligence (UAI), 2021‏
2262021
Reliable post hoc explanations: Modeling uncertainty in explainability‏
D Slack, A Hilgard, S Singh, H Lakkaraju‏
Advances in neural information processing systems 34, 9391-9404, 2021‏
2252021
Identifying unknown unknowns in the open world: Representations and policies for guided exploration‏
H Lakkaraju, E Kamar, R Caruana, E Horvitz‏
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017‏
2142017
The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables‏
H Lakkaraju, J Kleinberg, J Leskovec, J Ludwig, S Mullainathan‏
Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017‏
2132017
What's in a name? understanding the interplay between titles, content, and communities in social media‏
H Lakkaraju, J McAuley, J Leskovec‏
Proceedings of the international AAAI conference on web and social media 7 …, 2013‏
2022013
Openxai: Towards a transparent evaluation of model explanations‏
C Agarwal, S Krishna, E Saxena, M Pawelczyk, N Johnson, I Puri, M Zitnik, ...‏
Advances in neural information processing systems 35, 15784-15799, 2022‏
1862022
Counterfactual Explanations Can Be Manipulated‏
D Slack, S Hilgard, H Lakkaraju, S Singh‏
Advances in Neural Information Processing Systems (NeurIPS), 2021‏
1702021
Certifying llm safety against adversarial prompting‏
A Kumar, C Agarwal, S Srinivas, AJ Li, S Feizi, H Lakkaraju‏
arXiv preprint arXiv:2309.02705, 2023‏
1642023
Evaluating explainability for graph neural networks‏
C Agarwal, O Queen, H Lakkaraju, M Zitnik‏
Nature Scientific Data 10 (1), 144, 2023‏
1492023
Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments‏
H Lakkaraju, C Bhattacharya, I Bhattacharya, S Merugu‏
SIAM International Conference on Data Mining, 2011‏
1442011
Learning cost-effective and interpretable treatment regimes‏
H Lakkaraju, C Rudin‏
AISTATS, 2017, 2017‏
140*2017
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning‏
H Lakkaraju, R Socher, C Manning‏
NIPS Workshop on Deep Learning and Representation Learning, 2014‏
1252014
يتعذر على النظام إجراء العملية في الوقت الحالي. عاود المحاولة لاحقًا.
مقالات 1–20